Pandas Ge Scalar Int

Pandas Ge Scalar Int is the integer-only sibling of Pandas Ge Scalar Float: every cell that's greater than or equal to the integer you set comes back True, everything else False. Same threshold-filter job, same boolean-mask output - just without the decimal point, which is genuinely the right tool when your data is whole numbers anyway.

Think counts, ranks, ages, and years. "Keep rows where the count is at least 5." "Flag players who appeared in 100 or more games." Those are integer comparisons, and typing an int threshold into an int node removes the subtle ick of comparing floats that were never meant to be floats. It's also the node you want for inclusive lower bounds on ordinal data - >= 3 on a 1-to-5 rating scale means "3, 4, or 5," which is a natural way to cut a survey column.

How it works

dataframe.ge(number) with an int. Because the threshold is an integer, a float-typed column holding whole numbers still matches correctly - 4.0 >= 4 is True in pandas. NaN again compares as neither, coming back False, which is the sensible default for "did this meet the threshold" questions. The number input spans the full 32-bit signed range, so nothing realistic will hit the limits.

The output is a boolean mask like every comparison node in the pack, so the wiring is the same: into Pandas Boolean Index to filter rows, or into a show node to inspect where the threshold binds. Pair it with Pandas Eq Scalar Int if you need an exact-match branch and this for the range branch - together they cover "equals this code" and "at least this value."

Inputs and outputs

• dataframe (required, DATAFRAME) - the frame to test.
• number (required, INT, default 0) - the inclusive integer threshold.
• DATAFRAME output - the boolean mask.

Installing it

One install for the whole pack. ComfyUI Manager → search "Data analysis" → install ComfyUI-Data-Analysis → restart → reload. Or:

cd ComfyUI/custom_nodes
git clone https://github.com/HowToSD/ComfyUI-Data-Analysis
mv ComfyUI-Data-Analysis data-analysis
pip install -r data-analysis/requirements.txt

Requirements: pandas, matplotlib, seaborn, scipy, scikit-learn, openpyxl, lxml. No GPU, no model files. The pack is Hide Inada's (HowToSD) project for doing structured-data analysis in the ComfyUI graph, and it's been stable since late 2024 - install it once, get all the comparison nodes free.